Image detection device and image detection method

ABSTRACT

An image detection device and an image detection method are provided. The image detection method includes: obtaining an image, where the image includes an object; adjusting a first size of the image to generate an adjusted image; generating a first divided image and a second divided image according to the image; and detecting the object in the image based on a plurality of input images, where the plurality of input images includes the first divided image, the second divided image, and the adjusted image.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the priority benefit of Taiwan applicationserial no. 109118244, filed on Jun. 1, 2020. The entirety of theabove-mentioned patent application is hereby incorporated by referenceherein and made a part of this specification.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The disclosure relates to an electronic device and a method thereof, inparticular, to an image detection device and an image detection methodthereof.

2. Description of Related Art

With the advancement of technology, image detection technology is widelyused in different fields, such as quality management, trafficmanagement, or face detection. The results of image detection are oftenrelated to the computing power used for image processing and theresolution of the image. To improve the accuracy of image detection,users often need to purchase more hardware devices, for example, topurchase a camera with a higher resolution camera or higher capacitymemory.

However, upgrading the hardware device to improve the accuracy of imagedetection consumes a lot of costs. Based on this, how to propose analgorithm to improve the accuracy of image detection is one of the goalsof those skilled in the art.

SUMMARY OF THE INVENTION

The disclosure provides an image detection device and an image detectionmethod, which can detect an object in a far distance in an image withoutincreasing memory usage, clock frequency, or power consumption.

The image detection device of the disclosure includes a processor, astorage medium, and a transceiver. The storage medium stores multiplemodules. The processor is coupled to the storage medium and thetransceiver, and accesses and executes the multiple modules, where themultiple modules include a data collection module, an image processingmodule, and an image detection module. The data collection moduleobtains an image through the transceiver, where the image includes anobject. The image processing module adjusts a first size of the image togenerate an adjusted image and generates a first divided image and asecond divided image based on the image. The image detection moduledetects the object in the image based on multiple input images, wherethe multiple input images include the first divided image, the seconddivided image, and the adjusted image.

In an embodiment of the disclosure, the above image processing moduledivides a first frame of the image to generate the first divided imageand divides a second frame of the image to generate the second dividedimage, where the first frame is different from the second frame.

In an embodiment of the disclosure, the above image processing moduledivides a first frame of the image to generate the first divided imageand adjusts the first size of a third frame of the image to generate anadjusted image, where the first frame is different from the third frame.

In an embodiment of the disclosure, the above multiple input imagesrespectively correspond to different frames, where the image detectionmodule detects the object in the first frame of the image based on atleast one first candidate window corresponding to the first frame in themultiple input images.

In an embodiment of the disclosure, the above image detection moduledetects the object in the second frame of the image based on the atleast one first candidate window and at least one second candidatewindow corresponding to the second frame in the multiple input images.

In an embodiment of the disclosure, the above image detection moduledetermines a target frame based on the at least one candidate window anddetects the object in the second frame of the image based on the targetframe and at least one second candidate window corresponding to thesecond frame in the multiple input images.

In an embodiment of the disclosure, the above image processing moduleadjusts the first size of the image to generate a second adjusted imageand divides the second adjusted image to generate the first dividedimage and the second divided image.

In an embodiment of the disclosure, the above image processing moduleadjusts the first size of the image based on a reference image togenerate a third adjusted image, divides the third adjusted image basedon a size of a reference object in the reference image corresponding tothe object to generate a third divided image, and adjusts a second sizeof the third divided image to generate the first divided image, where afirst aspect ratio of the reference image is the same as a second aspectratio of any one of the multiple input images.

The image detection method of the disclosure includes: obtaining animage, where the image includes an object; adjusting a first size of theimage to generate an adjusted image; generating a first divided imageand a second divided image based on the image; and detecting the objectin the image based on multiple input images, where the multiple inputimages include: the first divided image, the second divided image, andthe adjusted image.

In an embodiment of the disclosure, the above steps of generating thefirst divided image and the second divided image based on the imageinclude: dividing a first frame of the image to generate the firstdivided image and dividing a second frame of the image to generate thesecond divided image, where the first frame is different from the secondframe.

In an embodiment of the disclosure, the above step of generating thefirst divided image and the second divided image based on the imageincludes: dividing the first frame of the image to generate the firstdivided image and adjusting the first size of a third frame to generatean adjusted image, where the first frame is different from the thirdframe.

In an embodiment of the disclosure, the above multiple input imagesrespectively correspond to different frames, where the step of detectingan object in the image based on the multiple input images includes:detecting an object in the first frame in the image based on at leastone first candidate window corresponding to the first frame in themultiple input images.

In an embodiment of the disclosure, the above step of detecting theobject in the image based on the multiple input images includes:adjusting the object in the second frame of the image based on at leastone first candidate window and at least one second candidate windowcorresponding to the second frame in the multiple input images.

In an embodiment of the disclosure, the above step of detecting theobject in the image based on the multiple input images includes:determining a target frame based on the at least one first candidatewindow and detecting the object in the second frame of the image basedon the target frame and the at least one second candidate windowcorresponding to the second frame in the multiple input images.

In an embodiment of the disclosure, the above step of generating thefirst divided image and the second divided image based on the imageincludes: adjusting the first size of the image to generate a secondadjusted image and dividing the second adjusted image to generate thefirst divided image and the second divided image.

In an embodiment of the disclosure, the above steps of generating thefirst divided image and the second divided image based on the imageinclude: adjusting the first size of the image based on a referenceimage to generate a third adjusted image; dividing the third adjustedimage based on a size of a reference object in the reference imagecorresponding to the object to generate a third divided image; andadjusting a second size of the third divided image to generate the firstdivided image, where a first aspect ratio of the reference image is thesame as a second aspect ratio of any of the multiple input images.

Based on the above, the image detection device of the disclosure candetect the object that is closer in distance in the image by usingadjusted images generated by enlarging the image, and can detect theobject that is farther apart in the image by using divided images. Usingdivided images instead of all images as the input images for imagedetection enables the image detection device to detect an object fartheraway in the image (that is, small or unclear object in the image).

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are included to provide a furtherunderstanding of the invention, and are incorporated in and constitute apart of this specification. The drawings illustrate embodiments of theinvention and, together with the description, serve to explain theprinciples of the invention.

FIG. 1 is a schematic diagram of an image detection device according toan embodiment of the disclosure.

FIG. 2 is a schematic diagram of generating an adjusted image accordingto an embodiment of the disclosure.

FIG. 3 is a schematic diagram of generating divided images according toan embodiment of the disclosure.

FIGS. 4A and 4B are schematic diagrams of generating divided imagesaccording to another embodiment of the disclosure.

FIG. 5 is a schematic diagram of a reference image according to anembodiment of the disclosure.

FIG. 6 is a schematic diagram of detecting an object in an image basedon candidate windows (candidate bounding windows) of different framesaccording to an embodiment of the disclosure.

FIG. 7 is a schematic diagram of detecting an object in an image basedon a target windows and candidate windows according to an embodiment ofthe disclosure.

FIG. 8 is a schematic diagram of the image detection device according toan embodiment of the disclosure.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the present preferredembodiments of the invention, examples of which are illustrated in theaccompanying drawings. Wherever possible, the same reference numbers areused in the drawings and the description to refer to the same or likeparts.

In order to make the content of the disclosure easier to understand, thefollowing embodiments are given as examples on which the disclosure canindeed be implemented. In addition, wherever possible,elements/components/steps using the same reference numbers in thedrawings and embodiments represent the same or similar components.

FIG. 1 is a schematic diagram of an image detection device 100 accordingto an embodiment of the disclosure. An image detection device 100 isused to detect one or more objects from an image. The image detectiondevice 100 includes a processor 110, a storage medium 120, and atransceiver 130.

The processor 110 may be, for example, a central processing unit (CPU)or other programmable general-purpose or special-purpose control unit(micro control unit, MCU), microprocessor, digital signal processor(DSP), programmable controller, application specific integrated circuit(ASIC), graphics processing unit (GPU), image signal processor (ISP),image processing unit (IPU), arithmetic logic unit (ALU), complexprogrammable logic device (CPLD), field programmable gate array (FPGA),or other similar components or a combination of the components. Theprocessor 110 may be coupled to the storage medium 120 and thetransceiver 130, and accesses and executes multiple modules and variousapplications stored in the storage medium 120.

The storage medium 120 may be, for example, any type of fixed orremovable random access memory (RAM), read-only memory (ROM), flashmemory, hard disk drive (HDD), solid state drive (SSD), or other similarcomponents or a combination of the components, and is used to store themultiple modules or various applications that can be executed by theprocessor 110. In this embodiment, the storage medium 120 may store themultiple modules including a data collection module 121, an imageprocessing module 122, and an image detection module 123, the functionsof which will be described later.

The transceiver 130 transmits and receives signals in a wireless orwired manner. The transceiver 130 may also perform operations such aslow noise amplification, impedance matching, frequency mixing, up ordown frequency conversion, filtering, amplification, and the like.

The data collection module 121 may obtain an image 200 through thetransceiver 130. For example, the transceiver 130 may be connected to acamera or a cloud server. The data collection module 121 may obtain theimage 200 from the camera or the cloud server through the transceiver130.

In an embodiment, due to the limitation of the hardware structure of theimage detection device 100, the image detection module 123 used todetect the object in the image only supports input images of a specificsize. To enable the image detection module 123 to efficiently detect theobject in the image 200, the image processing module 122 may performimage processing on the image 200 in advance to generate an input imagewith a size suited for the image detection module 123.

Specifically, the image processing module 122 may adjust the size of theimage 200 to generate an adjusted image 300, as shown in FIG. 2 . FIG. 2is a schematic diagram of generating an adjusted image 300 according toan embodiment of the disclosure. Assuming that the size of the inputimage for the image detection module 123 is l_(i)×w_(i) and the size ofthe image 200 is l₀×w₀, the image processing module 122 may scale theimage 200 to generate the adjusted image 300 of size l_(i)×w_(i). Inthis embodiment, the adjusted image 300 corresponds, for example, to aframe #t₀ of the image 200. In other words, the image processing 122 mayscale frame #t₀ of the image 200 to generate the adjusted image 300.

The adjusted image 300 may include all the information in the image 200and is better suited for detecting the object that is closer in distancein the image 200. For example, assuming that the size l_(i)×w_(i) of theadjusted image 300 is 320×224, the image processing module 122 canaccurately detect the object within a distance of 6 meters in the image200 based on the adjusted image 300.

On the other hand, in order to detect an object farther away in theimage 200, the image processing module 122 may generate multiple dividedimages based on the image 200, where each of the multiple divided imagesmay include some information of the image 200. FIG. 3 is a schematicdiagram of generating divided images 410 and 420 according to anembodiment of the disclosure. If the image processing module 122 wantsto generate two divided images corresponding to the image 200 to use thetwo divided images as the input images for the image detection module123, the image processing module 122 may first adjust the size of theimage 200 to generate an adjusted image 400 (the size of the adjustedimage 400 l₁×w₁ is equal to 2l_(i)×w_(i), for example), and then dividethe adjusted image 400 to generate a divided image 410 and a dividedimage 420 each of size l_(i)×w_(i), as shown in FIG. 3 , where w_(i) maybe less than or equal to w₁.

It is worth noting that if the image 200 may be directly divided intothe divided image 410 and the divided image 420 each of size suited forthe input image (for example: the size l₀×w₀ of the image 200 is equalto 2l_(i)×w₁), then the image processing module 122 does not need togenerate the adjusted image 400. The image processing module 122 maydirectly divide the image 200 to generate the divided image 410 and thedivided image 420.

In this embodiment, the divided image 410 corresponds, for example, toframe #t₁ of the image 200, and the divided image 420 corresponds, forexample, to frame #t₂ of the image 200, where frames #t₀, #t₁, and #t₂are not the same. In other words, the image processing module 122 maygenerate the divided image 410 based on frame #t₁ of the image 200, andmay generate the divided image 420 based on frame #t₂ of the image 200.

The divided image 410 (or the divided image 420) may include someinformation of the image 200 and is better suited for detecting theobject that is farther away in the image 200. For example, assuming thatthe size l_(i)×w_(i) of the divided image 410 is 320×224, the imageprocessing module 122 can accurately detect the object within a distanceof 11 meters in the image 200 based on the divided image 410.

According to the embodiment shown in FIG. 3 , if the object is locatedat the junction of the multiple divided images (for example, the dividedimages 410 and 420), the object may not be successfully detected by theimage detection module 123. In response to this, FIGS. 4A and 4B areschematic diagrams of generating the divided images according to anotherembodiment of the disclosure.

When the image processing module 122 generates two divided images basedon the image 200, the two divided images each need to contain anoverlapping part to prevent the object located at the junction of thedivided images from being undetected, where the overlapping part may beadjusted based on the type of object the user wants to detect.Specifically, the image processing module 122 may select a correspondingreference image based on the type of object the user wants to detect(for example: from multiple images pre-stored in the storage medium 120)and decide how to divide the image 200 based on the reference image.FIG. 5 is a schematic diagram of a reference image 700 according to anembodiment of the disclosure. For example, assuming that the user wantsto detect a person in the image 200, the image processing module 122 mayselect the reference image 700 corresponding to the person, where thesize of the reference image 700 is l_(r)×w_(r) and the reference image700 may include at least one reference object 1. It is worth noting thatthe aspect ratio of the reference image 700 may be the same as theaspect ratio of the input image for the image detection module 123.

Please refer to FIGS. 4A and 5 at the same time. The image processingmodule 122 may adjust the size of the image 200 based on the size of thereference image 700 to generate an adjusted image 500, where the size ofthe adjusted image 500 may be the same as the size of the referenceimage 700. Next, the image processing module 122 may divide the adjustedimage 500 based on the size of the reference object 1 in the referenceimage 700 to generate a divided image 510 and a divided image 520. In anembodiment, if the length of the reference object 1 in the referenceimage 700 is 1 d, the two adjacent divided images 510 and 520 of theadjusted image 500 each at least need to include an overlapping area 530of a length of 1 d. In this way, it can be ensured that the object inthe adjusted image 500 corresponding to the reference object 1 can becompletely included in at least one of the divided image 510 or thedivided image 520.

After the divided image 510 and the divided image 520 are generated, theimage processing module 122 may adjust the sizes of the divided image510 and the divided image 520 to generate multiple input images for theimage detection module 123. Taking FIG. 4B as an example, the imageprocessing module 122 may adjust the size of the divided image 510 tol_(i)×w_(i), thereby generating the divided image 410. Similarly, theimage processing module 122 may adjust the size of the divided image 520to l_(i)×w_(i), thereby generating the divided image 420.

After the adjusted image 300, the divided image 410, and the dividedimage 420 are generated, the adjusted image 300, the divided image 410,and the divided image 420 may be used as the multiple input images to beinput to the image detection module 123, and the image detection module123 can detect the object in the image 200 based on the multiple inputimages.

FIG. 6 is a schematic diagram of detecting the object in the image 200based on candidate windows of different frames according to anembodiment of the disclosure. The image detection module 123 canrecognize the object in frame #t₀ of the image 200 based on the adjustedimage 300 corresponding to frame #t₀ of the image 200. Specifically, inprocess P1, the image detection module 123 may use image recognitiontechnology to detect at least one object in the adjusted image 300,thereby generating at least one candidate window corresponding to frame#t0 in the input image (i.e. the adjusted image 300).

Then, in process P2, the image detection module 123 may determine atleast one candidate window to be used to detect the object in frame #t₀of the image 200 based on at least one candidate window corresponding toframe #t₀ in the input image (i.e. the adjusted image 300) and at leastone candidate window corresponding to the previous frame of frame #t₀ inthe input image. Since frame #t₀ in this embodiment is the first frame,the image detection module may determine the at least one candidatewindow corresponding to frame #t₀ in the image 200 based only on the atleast one candidate window corresponding to frame #t₀ in the input image(i.e. the adjusted image 300). For example, the at least one candidatewindow to be used to detect the object in frame #t₀ of the image 200 mayinclude a candidate window 601 and a candidate window 602 correspondingto the adjusted image 300.

Then, in process P3, the image detection module 123 may detect theobject in frame #0 of the image 200 based on the candidate window 601and the candidate window 602. For example, the image detection module123 may generate a target window 600 based on the candidate window 601and the candidate window 602 according to a non-maximum suppression(NMS) algorithm. Finally, the image detection 123 can recognize theobject in frame #t₀ of the image 200 based on the target window 600.

Similarly, the image detection module 123 can recognize the object inframe #t₁ of the image 200 based on the divided image 410 correspondingto frame #t₁ of the image 200. Specifically, in process P1, the imagedetection module 123 may use image recognition technology to detect atleast one object in the divided image 410, thereby generating at leastone candidate window corresponding to frame #t₁ in the input image (i.e.the divided image 410).

Then, in process P2, the image detection module 123 may determine atleast one candidate window to be used to detect the object in frame #t₁of the image 200 based on at least one candidate window corresponding toframe #t₁ in the input image (i.e. the divided image 410) and at leastone candidate window corresponding to frame #t₀ in the input image (i.e.the adjusted image 300). For example, the at least one candidate windowused to detect the object in frame #t₁ of the image 200 may include thecandidate window 601 and the candidate window 602 corresponding to theadjusted image 300 and a candidate window 611, a candidate window 612,and a candidate window 613 corresponding to the divided image 410.

Then, in process P3, the image detection module 123 can detect theobject in frame #t₁ of the image 200 based on the candidate windows 601,602, 611, 612, and 613. For example, the image detection module 123 maygenerate a target window 610 based on the candidate windows 601, 602,and 611 according to the NMS algorithm, and generate a target window 620based on the candidate windows 612 and 613 according to the NMSalgorithm. Finally, the image detection 123 can recognize the object inframe #t₁ of the image 200 based on the target windows 610 and 620.

Similarly, the image detection module 123 can recognize the object inframe #t₂ of the image 200 based on the divided image 420 correspondingto frame #t₂ of the image 200. Specifically, in process P1, the imagedetection module 123 may use image recognition technology to detect atleast one object in the divided image 420, thereby generating at leastone candidate window corresponding to frame #t₂ in the input image (i.e.the divided image 420).

Then, in process P2, the image detection module 123 may determine atleast one candidate window to be used to detect the object in frame #t₂of the image 200 based on at least one candidate window corresponding toframe #t₂ in the input image (i.e. the divided image 420) and at leastone candidate window corresponding to frame #t₁ of the input image (i.e.the divided image 410). For example, the at least one candidate windowused to detect the object in frame #t₂ of the image 200 may include thecandidate windows 611, 612 and 613 corresponding to the divided image410; and a candidate window 621 and a candidate window 622 correspondingthe divided image 420.

Then, in process P3, the image detection module 123 can detect theobject in frame #t₂ of the image 200 based on the candidate windows 611,612, 613, 621, and 622. For example, the image detection module 123 maygenerate a target window 640 based on the candidate window 611 accordingto the NMS algorithm, generate a target window 620 based on thecandidate windows 612 and 613 according to the NMS algorithm, andgenerate a target 630 based on the candidate windows 621 and 622according to the NMS algorithm. Finally, the image detection 123 canrecognize the object in frame #t₂ of the image 200 based on the targetwindows 620, 630 and 640.

FIG. 7 is a schematic diagram of detecting the object in the image 200based on target windows and candidate windows according to an embodimentof the disclosure. The image detection module 123 can recognize theobject in frame #t₀ of the image 200 based on the adjusted image 300corresponding to frame #t₀ of the image 200. Specifically, in processP1, the image detection module 123 may use image recognition technologyto detect at least one object in the adjusted image 300, therebygenerating at least one candidate window corresponding to frame #t₀ inthe input image (i.e. the adjusted image 300).

Then, in process P2, the image detection module 123 may determine atleast one candidate window to be used to detect the object in frame #t₀of the image 200 based on at least one candidate window corresponding toframe #t₀ in the input image (i.e. the adjusted image 300) and at leastone target window corresponding to the previous frame of frame #t₀ inthe input image. Since frame #t₀ in this embodiment is the first frame,the image detection module may determine the at least one candidatewindow corresponding to frame #t₀ in the image 200 based only on the atleast one candidate window corresponding to frame #t₀ in the input image(i.e. the adjusted image 300). For example, the at least one candidatewindow to be used to detect the object in frame #t0 of the image 200 mayinclude a candidate window 701 and a candidate window 702 correspondingto the adjusted image 300.

Then, in process P3, the image detection module 123 may detect theobject in frame #t₀ of the image 200 based on the candidate window 701and the candidate window 702. For example, the image detection module123 may generate a target window 700 based on the candidate windows 701and 702 according to the NMS algorithm. Finally, the image detection 123can recognize the object in frame #t₀ of the image 200 based on thetarget window 700.

Similarly, the image detection module 123 can recognize the object inframe #t₁ of the image 200 based on the divided image 410 correspondingto frame #t₁ of the image 200. Specifically, in process P1, the imagedetection module 123 may use image recognition technology to detect atleast one object in the divided image 410, thereby generating at leastone candidate window of corresponding to frame #t₁ in the input image(i.e. the divided image 410).

Then, in process P2, the image detection module 123 may determine atleast one candidate window to be used to detect the object in frame #t₁of the image 200 based on at least one candidate window corresponding toframe #t₁ in the input image (i.e. the divided image 410) and at leastone target window corresponding to frame #t₀ in the input image (i.e.the adjusted image 300). For example, the at least one candidate windowto be used to detect the object in frame #t₁ of the image 200 mayinclude a target window 700 corresponding to the adjusted image 300; anda candidate window 711, a candidate window 712, and a candidate window713 corresponding to the divided image 410.

Then, in process P3, the image detection module 123 can detect theobject in frame #t₁ of the image 200 based on the target window 700 andthe candidate windows 711, 712, and 713. For example, the imagedetection module 123 may generate a target window 710 based on thetarget window 700 and the candidate 711 according to the NMS algorithm,and generate a target window 720 based on the candidate windows 712 and713 according to the NMS algorithm.

Finally, the image detection 123 can recognize the object in frame #t₁of the image 200 based on the target windows 710 and 720.

Similarly, the image detection module 123 can recognize the object inframe #t₂ of the image 200 based on the divided image 420 correspondingto frame #t₂ of the image 200. Specifically, in process P1, the imagedetection module 123 may use image recognition technology to detect atleast one object in the divided image 420, thereby generating at leastone candidate window corresponding to frame #t₂ in the input image (i.e.the divided image 420).

Then, in process P2, the image detection module 123 may determine atleast one candidate window to be used to detect the object in frame #t₂of the image 200 based on at least one candidate window corresponding toframe #t₂ in the input image (i.e. the divided image 420) and at leastone target window corresponding to frame #t1 in the input image (i.e.the divided image 410). For example, the at least one candidate windowto be used to detect the object in frame #t₂ of the image 200 mayinclude the target windows 710 and 720 corresponding to the dividedimage 410 and candidate windows 721 and 722 corresponding to the dividedimage 420.

Then, in process P3, the image detection module 123 may detect theobject in frame #t₂ of the image 200 based on the target windows 710 and720 and the candidate windows 721 and 722. For example, the imagedetection module 123 may generate a target window 730 based on thecandidate windows 721 and 722 according to the NMS algorithm, generate atarget window 740 based on the target window 710 according to the NMSalgorithm, and generate a target window 750 based on the target window720 according to the NMS algorithm. Finally, the image detection 123 canidentify the object in frame #t₂ of the image 200 based on the targetwindows 730, 740, and 750.

FIG. 8 is a schematic diagram of an image detection device according toan embodiment of the disclosure, where the image detection method can beimplemented by the image detection device 100 shown in FIG. 1 . Step 801includes obtaining an image, where the image includes an object. Step802 includes adjusting a first size of the image to generate an adjustedimage. Step 803 includes generating a first divided image and a seconddivided image based on the image. Step 804 includes detecting the objectin the image based on multiple input images, where the multiple inputimages include the first divided image, the second divided image, andthe adjusted image.

In summary, the disclosure can convert an image into multiple imagessuch as adjusted images and divided images. The image detection devicecan use the adjusted images generated by enlarging the image to detectthe object that is closer in distance in the image. In addition, theimage detection device can use the divided images to detect the objectfarther away in the image. In any frame of the image, the imagedetection device detects only one of the multiple input images.Therefore, the image detection device does not require a lot ofcomputing power, memory capacity, clock frequency or power to achievenear-real-time detection. Using divided images instead of all images asthe input images for the image detection module enables the imagedetection module to obtain more details of the image and detect theobject farther away in the image.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the structure of the presentinvention without departing from the scope or spirit of the invention.In view of the foregoing, it is intended that the present inventioncover modifications and variations of this invention provided they fallwithin the scope of the following claims and their equivalents.

What is claimed is:
 1. An image detection device, comprising: atransceiver; a storage medium, storing a plurality of modules; and aprocessor, coupled to the storage medium and the transceiver and savingand executing the plurality of modules, wherein the plurality of modulescomprise: a data collection module, obtaining an image through thetransceiver, wherein the image comprises an object; an image processingmodule, adjusting a first size of the image to generate an adjustedimage and generating a first divided image and a second divided imagebased on the image; and an image detection module, detecting the objectin the image based on a plurality of input images, wherein the pluralityof input images comprise the first divided image, the second dividedimage, and the adjusted image, wherein the image processing moduledivides a first frame of the image to generate the first divided imageand adjusts the first size of a third frame of the image to generate theadjusted image, wherein the first frame is different from the thirdframe.
 2. The image detection device according to claim 1, wherein theimage processing module divides a second frame of the image to generatethe second divided image, wherein the first frame is different from thesecond frame.
 3. The image detection device according to claim 1,wherein the plurality of input images respectively correspond todifferent frames, wherein the image detection module detects the objectin the first frame of the image based on at least one first candidatewindow corresponding to the first frame in the plurality of the inputimages.
 4. The image detection device according to claim 3, wherein theimage detection module detects the object in the second frame of theimage based on the at least one first candidate window and at least onesecond candidate window corresponding to the second frame in theplurality of input images.
 5. The image detection device according toclaim 3, wherein the image detection module determines a target windowbased on the at least one first candidate window, and detects the objectin the second frame of the image based on the target frame and at leastone second candidate window corresponding to the second frame in theplurality of input images.
 6. The image detection device according toclaim 1, wherein the image processing module adjusts the first size ofthe image to generate a second adjusted image and divides the secondadjusted image to generate the first divided image and the seconddivided image.
 7. The image detection device according to claim 1,wherein the image processing module adjusts the first size of the imagebased on a reference image to generate a third adjusted image, dividesthe third adjusted image based on a size of a reference object in thereference image corresponding to the object, and adjusts a second sizeof the third divided image to generate the first divided image, whereina first aspect ratio of the reference image is the same as a secondaspect ratio of any one of the plurality of input images.
 8. An imagedetection method, comprising: obtaining an image, wherein the imagecomprises an object; adjusting a first size of the image to generate anadjusted image; generating a first divided image and a second dividedimage based on the image; and detecting the object in the image based ona plurality of input images, wherein the plurality of input imagescomprise the first divided image, the second divided image, and theadjusted image, wherein the step of generating the first divided imageand the second divided image based on the image comprises: dividing afirst frame of the image to generate the first divided image, and thestep of adjusting the first size of the image to generate the adjustedimage comprises: adjusting the first size of a third frame to generatethe adjusted image, wherein the first frame is different from the thirdframe.
 9. The image detection method according to claim 8, wherein thestep of generating the first divided image and the second divided imagebased on the image comprises: dividing a second frame of the image togenerate the second divided image, wherein the first frame is differentfrom the second frame.
 10. The image detection method according to claim8, wherein the plurality of input images respectively correspond todifferent frames, wherein the step of detecting the object in the imagebased on the plurality of input images comprises: detecting the objectin the first frame of the image based on at least one first candidatewindow corresponding to the first frame in the plurality of inputimages.
 11. The image detection method according to claim 10, whereinthe step of detecting the object in the image based on the plurality ofinput images comprises: detecting the object in the second frame of theimage based on the at least one first candidate window and at least onesecond candidate window corresponding to the second frame in theplurality of input images.
 12. The image detection method according toclaim 10, wherein the step of detecting the object in the image based onthe plurality of input images comprises: determining a target framebased on the at least one first candidate window, and detecting theobject in the second frame of the image based on the target frame and atleast one second candidate window corresponding to the second frame inthe plurality of input images.
 13. The image detection method accordingto claim 8, wherein the step of generating the first divided image andthe second divided image based on the image comprise: adjusting thefirst size of the image to generate a second adjusted image and dividingthe second adjusted image to generate the first divided image and thesecond divided image.
 14. The image detection method according to claim8, wherein the steps of generating the first divided image and thesecond divided image based on the image comprise: adjusting the firstsize of the image based on a reference image to generate a thirdadjusted image; dividing the third adjusted image based on a size of areference object in the reference image corresponding to the object togenerate a third divided image; and adjusting a second size of the thirddivided image to generate the first divided image, wherein a firstaspect ratio of the reference image is the same as a second aspect ratioof any one of the plurality of input images.